Systems and methods for server load balancing based on correlated events

ABSTRACT

Methods and systems for balancing online stores amongst servers. Detecting a flash sale associated with a first online store. Identifying an occurrence of a first event correlated to the flash sale associated with the first online store. Identifying a second online store associated with a second event corresponding to the first event. Responsive to identifying the second online store associated with the second event corresponding to the first event, moving the second online store from a first server to a second server.

FIELD

The present disclosure relates to computer-implemented e-commerce platforms and, in particular, to systems and methods for balancing request load among servers.

BACKGROUND

E-commerce systems may encounter heavy loads. For example, particularly heavy demand may need to be serviced in supporting flash sales. A flash sale may result in an online store suddenly receiving a high rate of order requests over a short period of time. Such events place a tremendous strain on e-commerce systems and third party resources, particularly when millions of customers concurrently attempt and/or complete checkout.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will be described, by way of example only, with reference to the accompanying figures wherein:

FIG. 1 is a block diagram of an e-commerce platform, according to one embodiment;

FIG. 2 is an example of a home page of an administrator, according to one embodiment;

FIG. 3 is a block diagram of an e-commerce platform, according to one embodiment;

FIG. 4 shows, in block diagram form, an example data facility of an e-commerce platform, according to one embodiment;

FIGS. 5A-F show, in graphic form, examples of historical levels of customer activity, according to one embodiment; and

FIG. 6 shows, in flowchart form, an example method of balancing online stores amongst servers, according to one embodiment.

DETAILED DESCRIPTION

In one aspect, the present application describes a computer-implemented method of balancing online stores amongst servers. The method may include detecting a flash sale associated with a first online store; identifying an occurrence of a first event correlated to the flash sale associated with the first online store; identifying a second online store associated with a second event corresponding to the first event; and responsive to identifying the second online store associated with the second event corresponding to the first event, moving the second online store from a first server to a second server.

In some implementations, identifying the second online store associated with the second event corresponding to the first event may include identifying a product or product category associated with the flash sale correlated to the occurrence of the first event; and identifying the second online store based on the product or product category.

In some implementations, identifying the second online store associated with the second event corresponding to the first event may include comparing a first profile associated with the first online store with a second profile associated with the second online store; and determining, based on the comparison, that at the respective profiles match with at least a threshold level of confidence.

In some implementations, the first event includes a modification to the first online store.

In some implementations, the first event is external to the first online store.

In some implementations, the first event corresponds to a natural disaster.

In some implementations, the first event corresponds to a recommendation of a product.

In some implementations, identifying the occurrence of the first event correlated to the flash sale includes obtaining data from a webpage or website external to the first online store.

In some implementations, the first and second online stores are different online stores.

In some implementations, the first and second online stores are the same online store.

In some implementations, the second event commences after the first event.

In some implementations, the second event includes a prediction based on the first event.

In some implementations, the prediction corresponds to the second online store associated with a second geographical region different from a first geographic region associated with the first online store.

In some implementations, detecting the flash sale includes detecting the flash sale in a first geographical region.

In some implementations, identifying an occurrence of a first event includes identifying an occurrence of a first event in a first geographic region.

In some implementations, identifying an occurrence of a first event correlated to the flash sale associated with the first online store includes mapping a product or product category associated with the flash sale to the first online store.

In another aspect, the present application describes a system including a processor; and a memory storing computer-executable instructions that, when executed by the processor, are to cause the processor detect a flash sale associated with a first online store; identify an occurrence of a first event correlated to the flash sale associated with the first online store; identify a second online store associated with a second event corresponding to the first event; and responsive to identifying the second online store associated with the second event corresponding to the first event, move the second online store from a first server to a second server.

In some embodiments, the instructions, when executed by the processor, may cause the processor to identify a product or product category associated with the flash sale correlated to the occurrence of the first event; and identify the second online store based on the product or product category.

In some embodiments, the instructions, when executed by the processor, may cause the processor to, compare a first profile associated with the first online store with a second profile associated with the second online store; and determine, based on the comparison, that at the respective profiles match with at least a threshold level of confidence.

In another aspect, the present application discloses a non-transitory, computer-readable medium storing processor-executable instructions that, when executed by one or more processors, are to cause the one or more processors to carry out at least some of the operations of a method described herein.

Other example embodiments of the present disclosure will be apparent to those of ordinary skill in the art from a review of the following detailed descriptions in conjunction with the drawings.

In the present application, the term “and/or” is intended to cover all possible combinations and sub-combinations of the listed elements, including any one of the listed elements alone, any sub-combination, or all of the elements, and without necessarily excluding additional elements.

In the present application, the phrase “at least one of . . . and . . . ” is intended to cover any one or more of the listed elements, including any one of the listed elements alone, any sub-combination, or all of the elements, without necessarily excluding any additional elements, and without necessarily requiring all of the elements.

An Example e-Commerce Platform

Although integration with a commerce platform is not required, in some embodiments, the methods disclosed herein may be performed on or in association with a commerce platform such as an e-commerce platform. Therefore, an example of a commerce platform will be described.

FIG. 1 illustrates an example e-commerce platform 100, according to one embodiment. The e-commerce platform 100 may be used to provide merchant products and services to customers. While the disclosure contemplates using the apparatus, system, and process to purchase products and services, for simplicity the description herein will refer to products. All references to products throughout this disclosure should also be understood to be references to products and/or services, including, for example, physical products, digital content (e.g., music, videos, games), software, tickets, subscriptions, services to be provided, and the like.

While the disclosure throughout contemplates that a ‘merchant’ and a ‘customer’ may be more than individuals, for simplicity the description herein may generally refer to merchants and customers as such. All references to merchants and customers throughout this disclosure should also be understood to be references to groups of individuals, companies, corporations, computing entities, and the like, and may represent for-profit or not-for-profit exchange of products. Further, while the disclosure throughout refers to ‘merchants’ and ‘customers’, and describes their roles as such, the e-commerce platform 100 should be understood to more generally support users in an e-commerce environment, and all references to merchants and customers throughout this disclosure should also be understood to be references to users, such as where a user is a merchant-user (e.g., a seller, retailer, wholesaler, or provider of products), a customer-user (e.g., a buyer, purchase agent, consumer, or user of products), a prospective user (e.g., a user browsing and not yet committed to a purchase, a user evaluating the e-commerce platform 100 for potential use in marketing and selling products, and the like), a service provider user (e.g., a shipping provider 112, a financial provider, and the like), a company or corporate user (e.g., a company representative for purchase, sales, or use of products; an enterprise user; a customer relations or customer management agent, and the like), an information technology user, a computing entity user (e.g., a computing bot for purchase, sales, or use of products), and the like. Furthermore, it may be recognized that while a given user may act in a given role (e.g., as a merchant) and their associated device may be referred to accordingly (e.g., as a merchant device) in one context, that same individual may act in a different role in another context (e.g., as a customer) and that same or another associated device may be referred to accordingly (e.g., as a customer device). For example, an individual may be a merchant for one type of product (e.g., shoes), and a customer/consumer of other types of products (e.g., groceries). In another example, an individual may be both a consumer and a merchant of the same type of product. In a particular example, a merchant that trades in a particular category of goods may act as a customer for that same category of goods when they order from a wholesaler (the wholesaler acting as merchant).

The e-commerce platform 100 provides merchants with online services/facilities to manage their business. The facilities described herein are shown implemented as part of the platform 100 but could also be configured separately from the platform 100, in whole or in part, as stand-alone services. Furthermore, such facilities may, in some embodiments, additionally or alternatively, be provided by one or more providers/entities.

In the example of FIG. 1 , the facilities are deployed through a machine, service or engine that executes computer software, modules, program codes, and/or instructions on one or more processors which, as noted above, may be part of or external to the platform 100. Merchants may utilize the e-commerce platform 100 for enabling or managing commerce with customers, such as by implementing an e-commerce experience with customers through an online store 138, applications 142A-B, channels 110A-B, and/or through point of sale (POS) devices 152 in physical locations (e.g., a physical storefront or other location such as through a kiosk, terminal, reader, printer, 3D printer, and the like). A merchant may utilize the e-commerce platform 100 as a sole commerce presence with customers, or in conjunction with other merchant commerce facilities, such as through a physical store (e.g., ‘brick-and-mortar’ retail stores), a merchant off-platform website 104 (e.g., a commerce Internet website or other Internet or web property or asset supported by or on behalf of the merchant separately from the e-commerce platform 100), an application 142B, and the like. However, even these ‘other’ merchant commerce facilities may be incorporated into or communicate with the e-commerce platform 100, such as where POS devices 152 in a physical store of a merchant are linked into the e-commerce platform 100, where a merchant off-platform website 104 is tied into the e-commerce platform 100, such as, for example, through ‘buy buttons’ that link content from the merchant off platform website 104 to the online store 138, or the like.

The online store 138 may represent a multi-tenant facility comprising a plurality of virtual storefronts. In embodiments, merchants may configure and/or manage one or more storefronts in the online store 138, such as, for example, through a merchant device 102 (e.g., computer, laptop computer, mobile computing device, and the like), and offer products to customers through a number of different channels 110A-B (e.g., an online store 138; an application 142A-B; a physical storefront through a POS device 152; an electronic marketplace, such, for example, through an electronic buy button integrated into a website or social media channel such as on a social network, social media page, social media messaging system; and/or the like). A merchant may sell across channels 110A-B and then manage their sales through the e-commerce platform 100, where channels 110A may be provided as a facility or service internal or external to the e-commerce platform 100. A merchant may, additionally or alternatively, sell in their physical retail store, at pop ups, through wholesale, over the phone, and the like, and then manage their sales through the e-commerce platform 100. A merchant may employ all or any combination of these operational modalities. Notably, it may be that by employing a variety of and/or a particular combination of modalities, a merchant may improve the probability and/or volume of sales. Throughout this disclosure the terms online store 138 and storefront may be used synonymously to refer to a merchant's online e-commerce service offering through the e-commerce platform 100, where an online store 138 may refer either to a collection of storefronts supported by the e-commerce platform 100 (e.g., for one or a plurality of merchants) or to an individual merchant's storefront (e.g., a merchant's online store).

In some embodiments, a customer may interact with the platform 100 through a customer device 150 (e.g., computer, laptop computer, mobile computing device, or the like), a POS device 152 (e.g., retail device, kiosk, automated (self-service) checkout system, or the like), and/or any other commerce interface device known in the art. The e-commerce platform 100 may enable merchants to reach customers through the online store 138, through applications 142A-B, through POS devices 152 in physical locations (e.g., a merchant's storefront or elsewhere), to communicate with customers via electronic communication facility 129, and/or the like so as to provide a system for reaching customers and facilitating merchant services for the real or virtual pathways available for reaching and interacting with customers.

In some embodiments, and as described further herein, the e-commerce platform 100 may be implemented through a processing facility. Such a processing facility may include a processor and a memory. The processor may be a hardware processor. The memory may be and/or may include a non-transitory computer-readable medium. The memory may be and/or may include random access memory (RAM) and/or persisted storage (e.g., magnetic storage). The processing facility may store a set of instructions (e.g., in the memory) that, when executed, cause the e-commerce platform 100 to perform the e-commerce and support functions as described herein. The processing facility may be or may be a part of one or more of a server, client, network infrastructure, mobile computing platform, cloud computing platform, stationary computing platform, and/or some other computing platform, and may provide electronic connectivity and communications between and amongst the components of the e-commerce platform 100, merchant devices 102, payment gateways 106, applications 142A-B, channels 110A-B, shipping providers 112, customer devices 150, point of sale devices 152, etc. In some implementations, the processing facility may be or may include one or more such computing devices acting in concert. For example, it may be that a plurality of co-operating computing devices serves as/to provide the processing facility. The e-commerce platform 100 may be implemented as or using one or more of a cloud computing service, software as a service (SaaS), infrastructure as a service (IaaS), platform as a service (PaaS), desktop as a service (DaaS), managed software as a service (MSaaS), mobile backend as a service (MBaaS), information technology management as a service (ITMaaS), and/or the like. For example, it may be that the underlying software implementing the facilities described herein (e.g., the online store 138) is provided as a service, and is centrally hosted (e.g., and then accessed by users via a web browser or other application, and/or through customer devices 150, POS devices 152, and/or the like). In some embodiments, elements of the e-commerce platform 100 may be implemented to operate and/or integrate with various other platforms and operating systems.

In some embodiments, the facilities of the e-commerce platform 100 (e.g., the online store 138) may serve content to a customer device 150 (using data 134) such as, for example, through a network connected to the e-commerce platform 100. For example, the online store 138 may serve or send content in response to requests for data 134 from the customer device 150, where a browser (or other application) connects to the online store 138 through a network using a network communication protocol (e.g., an internet protocol). The content may be written in machine readable language and may include Hypertext Markup Language (HTML), template language, JavaScript, and the like, and/or any combination thereof.

In some embodiments, online store 138 may be or may include service instances that serve content to customer devices and allow customers to browse and purchase the various products available (e.g., add them to a cart, purchase through a buy-button, and the like). Merchants may also customize the look and feel of their website through a theme system, such as, for example, a theme system where merchants can select and change the look and feel of their online store 138 by changing their theme while having the same underlying product and business data shown within the online store's product information. It may be that themes can be further customized through a theme editor, a design interface that enables users to customize their website's design with flexibility. Additionally or alternatively, it may be that themes can, additionally or alternatively, be customized using theme-specific settings such as, for example, settings as may change aspects of a given theme, such as, for example, specific colors, fonts, and pre-built layout schemes. In some implementations, the online store may implement a content management system for website content. Merchants may employ such a content management system in authoring blog posts or static pages and publish them to their online store 138, such as through blogs, articles, landing pages, and the like, as well as configure navigation menus. Merchants may upload images (e.g., for products), video, content, data, and the like to the e-commerce platform 100, such as for storage by the system (e.g., as data 134). In some embodiments, the e-commerce platform 100 may provide functions for manipulating such images and content such as, for example, functions for resizing images, associating an image with a product, adding and associating text with an image, adding an image for a new product variant, protecting images, and the like.

As described herein, the e-commerce platform 100 may provide merchants with sales and marketing services for products through a number of different channels 110A-B, including, for example, the online store 138, applications 142A-B, as well as through physical POS devices 152 as described herein. The e-commerce platform 100 may, additionally or alternatively, include business support services 116, an administrator 114, a warehouse management system, and the like associated with running an on-line business, such as, for example, one or more of providing a domain registration service 118 associated with their online store, payment services 120 for facilitating transactions with a customer, shipping services 122 for providing customer shipping options for purchased products, fulfillment services for managing inventory, risk and insurance services 124 associated with product protection and liability, merchant billing, and the like. Services 116 may be provided via the e-commerce platform 100 or in association with external facilities, such as through a payment gateway 106 for payment processing, shipping providers 112 for expediting the shipment of products, and the like.

In some embodiments, the e-commerce platform 100 may be configured with shipping services 122 (e.g., through an e-commerce platform shipping facility or through a third-party shipping carrier), to provide various shipping-related information to merchants and/or their customers such as, for example, shipping label or rate information, real-time delivery updates, tracking, and/or the like.

FIG. 2 depicts a non-limiting embodiment for a home page of an administrator 114. The administrator 114 may be referred to as an administrative console and/or an administrator console. The administrator 114 may show information about daily tasks, a store's recent activity, and the next steps a merchant can take to build their business. In some embodiments, a merchant may log in to the administrator 114 via a merchant device 102 (e.g., a desktop computer or mobile device), and manage aspects of their online store 138, such as, for example, viewing the online store's 138 recent visit or order activity, updating the online store's 138 catalog, managing orders, and/or the like. In some embodiments, the merchant may be able to access the different sections of the administrator 114 by using a sidebar, such as the one shown on FIG. 2 . Sections of the administrator 114 may include various interfaces for accessing and managing core aspects of a merchant's business, including orders, products, customers, available reports and discounts. The administrator 114 may, additionally or alternatively, include interfaces for managing sales channels for a store including the online store 138, mobile application(s) made available to customers for accessing the store (Mobile App), POS devices, and/or a buy button. The administrator 114 may, additionally or alternatively, include interfaces for managing applications (apps) installed on the merchant's account; and settings applied to a merchant's online store 138 and account. A merchant may use a search bar to find products, pages, or other information in their store.

More detailed information about commerce and visitors to a merchant's online store 138 may be viewed through reports or metrics. Reports may include, for example, acquisition reports, behavior reports, customer reports, finance reports, marketing reports, sales reports, product reports, and custom reports. The merchant may be able to view sales data for different channels 110A-B from different periods of time (e.g., days, weeks, months, and the like), such as by using drop-down menus. An overview dashboard may also be provided for a merchant who wants a more detailed view of the store's sales and engagement data. An activity feed in the home metrics section may be provided to illustrate an overview of the activity on the merchant's account. For example, by clicking on a ‘view all recent activity’ dashboard button, the merchant may be able to see a longer feed of recent activity on their account. A home page may show notifications about the merchant's online store 138, such as based on account status, growth, recent customer activity, order updates, and the like. Notifications may be provided to assist a merchant with navigating through workflows configured for the online store 138, such as, for example, a payment workflow, an order fulfillment workflow, an order archiving workflow, a return workflow, and the like.

The e-commerce platform 100 may provide for a communications facility 129 and associated merchant interface for providing electronic communications and marketing, such as utilizing an electronic messaging facility for collecting and analyzing communication interactions between merchants, customers, merchant devices 102, customer devices 150, POS devices 152, and the like, to aggregate and analyze the communications, such as for increasing sale conversions, and the like. For instance, a customer may have a question related to a product, which may produce a dialog between the customer and the merchant (or an automated processor-based agent/chatbot representing the merchant), where the communications facility 129 is configured to provide automated responses to customer requests and/or provide recommendations to the merchant on how to respond such as, for example, to improve the probability of a sale.

The e-commerce platform 100 may provide a financial facility 120 for secure financial transactions with customers, such as through a secure card server environment. The e-commerce platform 100 may store credit card information, such as in payment card industry data (PCI) environments (e.g., a card server), to reconcile financials, bill merchants, perform automated clearing house (ACH) transfers between the e-commerce platform 100 and a merchant's bank account, and the like. The financial facility 120 may also provide merchants and buyers with financial support, such as through the lending of capital (e.g., lending funds, cash advances, and the like) and provision of insurance. In some embodiments, online store 138 may support a number of independently administered storefronts and process a large volume of transactional data on a daily basis for a variety of products and services. Transactional data may include any customer information indicative of a customer, a customer account or transactions carried out by a customer such as, for example, contact information, billing information, shipping information, returns/refund information, discount/offer information, payment information, or online store events or information such as page views, product search information (search keywords, click-through events), product reviews, abandoned carts, and/or other transactional information associated with business through the e-commerce platform 100. In some embodiments, the e-commerce platform 100 may store this data in a data facility 134. Referring again to FIG. 1 , in some embodiments the e-commerce platform 100 may include a commerce management engine 136 such as may be configured to perform various workflows for task automation or content management related to products, inventory, customers, orders, suppliers, reports, financials, risk and fraud, and the like. In some embodiments, additional functionality may, additionally or alternatively, be provided through applications 142A-B to enable greater flexibility and customization required for accommodating an ever-growing variety of online stores, POS devices, products, and/or services. Applications 142A may be components of the e-commerce platform 100 whereas applications 142B may be provided or hosted as a third-party service external to e-commerce platform 100. The commerce management engine 136 may accommodate store-specific workflows and in some embodiments, may incorporate the administrator 114 and/or the online store 138.

Implementing functions as applications 142A-B may enable the commerce management engine 136 to remain responsive and reduce or avoid service degradation or more serious infrastructure failures, and the like.

Although isolating online store data can be important to maintaining data privacy between online stores 138 and merchants, there may be reasons for collecting and using cross-store data, such as, for example, with an order risk assessment system or a platform payment facility, both of which require information from multiple online stores 138 to perform well. In some embodiments, it may be preferable to move these components out of the commerce management engine 136 and into their own infrastructure within the e-commerce platform 100.

Platform payment facility 120 is an example of a component that utilizes data from the commerce management engine 136 but is implemented as a separate component or service. The platform payment facility 120 may allow customers interacting with online stores 138 to have their payment information stored safely by the commerce management engine 136 such that they only have to enter it once. When a customer visits a different online store 138, even if they have never been there before, the platform payment facility 120 may recall their information to enable a more rapid and/or potentially less-error prone (e.g., through avoidance of possible mis-keying of their information if they needed to instead re-enter it) checkout. This may provide a cross-platform network effect, where the e-commerce platform 100 becomes more useful to its merchants and buyers as more merchants and buyers join, such as because there are more customers who checkout more often because of the ease of use with respect to customer purchases. To maximize the effect of this network, payment information for a given customer may be retrievable and made available globally across multiple online stores 138.

For functions that are not included within the commerce management engine 136, applications 142A-B provide a way to add features to the e-commerce platform 100 or individual online stores 138. For example, applications 142A-B may be able to access and modify data on a merchant's online store 138, perform tasks through the administrator 114, implement new flows for a merchant through a user interface (e.g., that is surfaced through extensions/API), and the like. Merchants may be enabled to discover and install applications 142A-B through application search, recommendations, and support 128. In some embodiments, the commerce management engine 136, applications 142A-B, and the administrator 114 may be developed to work together. For instance, application extension points may be built inside the commerce management engine 136, accessed by applications 142A and 142B through the interfaces 140B and 140A to deliver additional functionality, and surfaced to the merchant in the user interface of the administrator 114.

In some embodiments, applications 142A-B may deliver functionality to a merchant through the interface 140A-B, such as where an application 142A-B is able to surface transaction data to a merchant (e.g., App: “Engine, surface my app data in the Mobile App or administrator 114”), and/or where the commerce management engine 136 is able to ask the application to perform work on demand (Engine: “App, give me a local tax calculation for this checkout”).

Applications 142A-B may be connected to the commerce management engine 136 through an interface 140A-B (e.g., through REST (REpresentational State Transfer) and/or GraphQL APIs) to expose the functionality and/or data available through and within the commerce management engine 136 to the functionality of applications. For instance, the e-commerce platform 100 may provide API interfaces 140A-B to applications 142A-B which may connect to products and services external to the platform 100. The flexibility offered through use of applications and APIs (e.g., as offered for application development) enable the e-commerce platform 100 to better accommodate new and unique needs of merchants or to address specific use cases without requiring constant change to the commerce management engine 136. For instance, shipping services 122 may be integrated with the commerce management engine 136 through a shipping or carrier service API, thus enabling the e-commerce platform 100 to provide shipping service functionality without directly impacting code running in the commerce management engine 136.

Depending on the implementation, applications 142A-B may utilize APIs to pull data on demand (e.g., customer creation events, product change events, or order cancelation events, etc.) or have the data pushed when updates occur. A subscription model may be used to provide applications 142A-B with events as they occur or to provide updates with respect to a changed state of the commerce management engine 136. In some embodiments, when a change related to an update event subscription occurs, the commerce management engine 136 may post a request, such as to a predefined callback URL. The body of this request may contain a new state of the object and a description of the action or event. Update event subscriptions may be created manually, in the administrator facility 114, or automatically (e.g., via the API 140A-B). In some embodiments, update events may be queued and processed asynchronously from a state change that triggered them, which may produce an update event notification that is not distributed in real-time or near-real time.

In some embodiments, the e-commerce platform 100 may provide one or more of application search, recommendation and support 128. Application search, recommendation and support 128 may include developer products and tools to aid in the development of applications, an application dashboard (e.g., to provide developers with a development interface, to administrators for management of applications, to merchants for customization of applications, and the like), facilities for installing and providing permissions with respect to providing access to an application 142A-B (e.g., for public access, such as where criteria must be met before being installed, or for private use by a merchant), application searching to make it easy for a merchant to search for applications 142A-B that satisfy a need for their online store 138, application recommendations to provide merchants with suggestions on how they can improve the user experience through their online store 138, and the like. In some embodiments, applications 142A-B may be assigned an application identifier (ID), such as for linking to an application (e.g., through an API), searching for an application, making application recommendations, and the like.

Applications 142A-B may be grouped roughly into three categories: customer-facing applications, merchant-facing applications, integration applications, and the like. Customer-facing applications 142A-B may include an online store 138 or channels 110A-B that are places where merchants can list products and have them purchased (e.g., the online store, applications for flash sales (e.g., merchant products or from opportunistic sales opportunities from third-party sources), a mobile store application, a social media channel, an application for providing wholesale purchasing, and the like). Merchant-facing applications 142A-B may include applications that allow the merchant to administer their online store 138 (e.g., through applications related to the web or website or to mobile devices), run their business (e.g., through applications related to POS devices), to grow their business (e.g., through applications related to shipping (e.g., drop shipping), use of automated agents, use of process flow development and improvements), and the like. Integration applications may include applications that provide useful integrations that participate in the running of a business, such as shipping providers 112 and payment gateways 106.

As such, the e-commerce platform 100 can be configured to provide an online shopping experience through a flexible system architecture that enables merchants to connect with customers in a flexible and transparent manner. A typical customer experience may be better understood through an embodiment example purchase workflow, where the customer browses the merchant's products on a channel 110A-B, adds what they intend to buy to their cart, proceeds to checkout, and pays for the content of their cart resulting in the creation of an order for the merchant. The merchant may then review and fulfill (or cancel) the order. The product is then delivered to the customer. If the customer is not satisfied, they might return the products to the merchant.

In an example embodiment, a customer may browse a merchant's products through a number of different channels 110A-B such as, for example, the merchant's online store 138, a physical storefront through a POS device 152; an electronic marketplace, through an electronic buy button integrated into a website or a social media channel). In some cases, channels 110A-B may be modeled as applications 142A-B. A merchandising component in the commerce management engine 136 may be configured for creating, and managing product listings (using product data objects or models for example) to allow merchants to describe what they want to sell and where they sell it. The association between a product listing and a channel may be modeled as a product publication and accessed by channel applications, such as via a product listing API. A product may have many attributes and/or characteristics, like size and color, and many variants that expand the available options into specific combinations of all the attributes, like a variant that is size extra-small and green, or a variant that is size large and blue. Products may have at least one variant (e.g., a “default variant”) created for a product without any options. To facilitate browsing and management, products may be grouped into collections, provided product identifiers (e.g., stock keeping unit (SKU)) and the like. Collections of products may be built by either manually categorizing products into one (e.g., a custom collection), by building rulesets for automatic classification (e.g., a smart collection), and the like. Product listings may include 2D images, 3D images or models, which may be viewed through a virtual or augmented reality interface, and the like.

In some embodiments, a shopping cart object is used to store or keep track of the products that the customer intends to buy. The shopping cart object may be channel specific and can be composed of multiple cart line items, where each cart line item tracks the quantity for a particular product variant. Since adding a product to a cart does not imply any commitment from the customer or the merchant, and the expected lifespan of a cart may be in the order of minutes (not days), cart objects/data representing a cart may be persisted to an ephemeral data store.

The customer then proceeds to checkout. A checkout object or page generated by the commerce management engine 136 may be configured to receive customer information to complete the order such as the customer's contact information, billing information and/or shipping details. If the customer inputs their contact information but does not proceed to payment, the e-commerce platform 100 may (e.g., via an abandoned checkout component) transmit a message to the customer device 150 to encourage the customer to complete the checkout. For those reasons, checkout objects can have much longer lifespans than cart objects (hours or even days) and may therefore be persisted. Customers then pay for the content of their cart resulting in the creation of an order for the merchant. In some embodiments, the commerce management engine 136 may be configured to communicate with various payment gateways and services 106 (e.g., online payment systems, mobile payment systems, digital wallets, credit card gateways) via a payment processing component. The actual interactions with the payment gateways 106 may be provided through a card server environment. At the end of the checkout process, an order is created. An order is a contract of sale between the merchant and the customer where the merchant agrees to provide the goods and services listed on the order (e.g., order line items, shipping line items, and the like) and the customer agrees to provide payment (including taxes). Once an order is created, an order confirmation notification may be sent to the customer and an order placed notification sent to the merchant via a notification component. Inventory may be reserved when a payment processing job starts to avoid over-selling (e.g., merchants may control this behavior using an inventory policy or configuration for each variant). Inventory reservation may have a short time span (minutes) and may need to be fast and scalable to support flash sales or “drops”, which are events during which a discount, promotion or limited inventory of a product may be offered for sale for buyers in a particular location and/or for a particular (usually short) time. The reservation is released if the payment fails. When the payment succeeds, and an order is created, the reservation is converted into a permanent (long-term) inventory commitment allocated to a specific location. An inventory component of the commerce management engine 136 may record where variants are stocked, and track quantities for variants that have inventory tracking enabled. It may decouple product variants (a customer-facing concept representing the template of a product listing) from inventory items (a merchant-facing concept that represents an item whose quantity and location is managed). An inventory level component may keep track of quantities that are available for sale, committed to an order or incoming from an inventory transfer component (e.g., from a vendor).

The merchant may then review and fulfill (or cancel) the order. A review component of the commerce management engine 136 may implement a business process merchant's use to ensure orders are suitable for fulfillment before actually fulfilling them. Orders may be fraudulent, require verification (e.g., ID checking), have a payment method which requires the merchant to wait to make sure they will receive their funds, and the like. Risks and recommendations may be persisted in an order risk model. Order risks may be generated from a fraud detection tool, submitted by a third-party through an order risk API, and the like. Before proceeding to fulfillment, the merchant may need to capture the payment information (e.g., credit card information) or wait to receive it (e.g., via a bank transfer, check, and the like) before it marks the order as paid. The merchant may now prepare the products for delivery. In some embodiments, this business process may be implemented by a fulfillment component of the commerce management engine 136. The fulfillment component may group the line items of the order into a logical fulfillment unit of work based on an inventory location and fulfillment service. The merchant may review, adjust the unit of work, and trigger the relevant fulfillment services, such as through a manual fulfillment service (e.g., at merchant managed locations) used when the merchant picks and packs the products in a box, purchase a shipping label and input its tracking number, or just mark the item as fulfilled. Alternatively, an API fulfillment service may trigger a third-party application or service to create a fulfillment record for a third-party fulfillment service. Other possibilities exist for fulfilling an order. If the customer is not satisfied, they may be able to return the product(s) to the merchant. The business process merchants may go through to “un-sell” an item may be implemented by a return component. Returns may consist of a variety of different actions, such as a restock, where the product that was sold actually comes back into the business and is sellable again; a refund, where the money that was collected from the customer is partially or fully returned; an accounting adjustment noting how much money was refunded (e.g., including if there was any restocking fees or goods that weren't returned and remain in the customer's hands); and the like. A return may represent a change to the contract of sale (e.g., the order), and where the e-commerce platform 100 may make the merchant aware of compliance issues with respect to legal obligations (e.g., with respect to taxes). In some embodiments, the e-commerce platform 100 may enable merchants to keep track of changes to the contract of sales over time, such as implemented through a sales model component (e.g., an append-only date-based ledger that records sale-related events that happened to an item).

Balancing Load Across Servers

FIG. 3 illustrates an example e-commerce platform 300 in block diagram form. The e-commerce platform 300 of FIG. 3 is illustrated as distinct from the e-commerce platform 100 of FIG. 1 for ease of illustration, but these e-commerce platforms may be implemented together as a single e-commerce platform. In other words, aspects of one of the e-commerce platforms 100 and 300 may be implemented as part of the other e-commerce platform.

The e-commerce platform 300 may be implemented as a software as a service. In other words, the e-commerce platform 300 may be delivered as a service by a service provider to a service consumer, namely, a merchant. The service provider may manage or control the installation, configuration, operation and maintenance of the software that implements the e-commerce platform 300. The service provider may also manage or control resources used by the e-commerce platform 300, such as servers and data facilities, or may configure the e-commerce platform 300 to use third-party resources. The service provided by the e-commerce platform may sometimes be referred to as an online store hosting service. A merchant may obtain a license to use the store hosting capabilities of a running instance of the e-commerce platform 300. The e-commerce platform may provide merchants with facilities for creating an online store 138.

The e-commerce platform 300 includes a router 302 and a plurality of servers 304 (shown individually as 304 a, 304 b, 304 c, 304 d). The servers 304 host a plurality of online stores. More than one online store may be hosted per server. Each of the servers 304 may also include one or more components of the e-commerce platform 100 of FIG. 1 in order to host online stores. For example, each of the servers 304 may include a respective running instance of the commerce management engine 136 of FIG. 1 .

The e-commerce platform 300 may further include a router 302. The router may receive, via the e-commerce platform and from a customer device, requests corresponding to an online store. The router may be configured to, upon receiving a request, identify an online store that corresponds to the request based on the request itself. For example, the router may analyze the contents of the request in order to determine a unique identifier corresponding to an online store. The request may be implemented as an HTTP request and include a destination domain name and one or more parameters. The destination domain name may uniquely identify a store. In some embodiments, the one or more parameters included in the request may also be used to uniquely identify an online store. By way of example, the request may include a name/value pair, for example, a parameter name “storeId” and a corresponding value “johnsapparel”, which may be a unique store identifier. The router may map the identified store to one of the servers 304. By way of example, a lookup table may, in at least some instances, be used to map online store identifiers to server identifiers. The router may then direct the request to the identified server. In this way, the router 302 may direct requests received by the e-commerce platform 300 from customer devices to one of the servers 304.

The e-commerce platform 300 may include a customer activity monitor 306 that monitors the level of customer activity on the router 302 or the servers 304. As an example, the customer activity monitor 306 may obtain data regarding the current traffic on the router 302 and on the servers 304. The customer activity monitor 306 may be configured to provide metrics for individual online stores hosted on the servers 304. The level of customer activity may vary from one online store to another online store.

The e-commerce platform 300 may include a scheduler 308 that is configured to trigger the performance of a store-specific action at a scheduled time. The action may include the launch of an online marketing campaign, the online release of a new product, moving an online store from one server to another, or such other action.

It will be appreciated that although the customer activity monitor 306 and scheduler 308 are illustrated as separate elements from the router 302 and the servers 304 for ease of explanation, they may be implemented as separate software applications or modules, or partially or completely together as one software application or module, or as part of a larger software application or module, within or outside the router 302 and the servers 304.

Reference is now made to FIG. 4 , which partially illustrates an example data facility 400 of an e-commerce platform in block diagram form. The data facility may be a data facility 134 of the example e-commerce platform 100 of FIG. 1 or the example e-commerce platform 300 of FIG. 3 or a data facility external to an e-commerce platform. Not all components of the data facility 400 are illustrated. The data facility 400 may include one or more data storage units. In some cases, the data storage may be in database format and may include one or more databases. The databases may be relational databases in some examples. The data facility 400 is illustrated as a single unit for ease of illustration, but may include a plurality of storage units.

The data facility 400 may contain various types of data including online store data, server data, and routing data. The online store data may include data corresponding to the online stores 138, the server data may include data corresponding to the servers 304, and the routing data may include data corresponding to the online stores 138 and servers 304.

The data facility 400 may store data regarding an online store in a store object 402. The store object 402 may be a data structure and may include details regarding an online store. Example details include a store identifier, a store name, a store category, a domain name, product catalog information, order information, scheduler information, policies and historical analytics data. The store identifier and domain name may be unique identifiers of an online store hosted on the e-commerce platform. The details may include data received by the e-commerce platform from a merchant device, such as product catalog information and may also include data generated by the system, such as a store identifier and historical analytics data.

A policy may generally refer to a data structure and/or other information. A policy may include a set of preferences, settings, rules, conditions or other criteria for defining the behaviour of operations of the e-commerce platform or a component or function thereof. The policy may include system-wide, store-specific, and/or server-specific policy data that is customizable by a merchant and/or system operator. The policy may include a merchant defined policy or a subscription plan (e.g. a fee structure indicating the level of service provided by an e-commerce platform to an online store). In some embodiments, the policy may be configured, for example, by a merchant via a user interface provided by an e-commerce platform.

A policy may include language and currency settings for an online store. In particular, the language settings may include a list of languages in which the text included in a user interface corresponding to the online store may be displayed. The currency settings may include a list of currencies in which product prices included in the user interface may be displayed. The currency settings may also include a list of currencies that are accepted by the online store for order payments.

The data facility 400 may store data regarding a server of the e-commerce platform in a server object 404. The server object 404 may be a data structure and may include details regarding a server. Example details include a server identifier, hostname, IP address, and rated capacity corresponding to the server. The server identifier, hostname and IP address may be unique identifiers of a server.

The term “rated capacity” may generally refer to a maximum load, or level of activity, a server is designed to withstand. If the load or activity on the server is greater than that for which the server was designed, the server may be referred to as being “overloaded”. A server that is overloaded may experience complete failure or a degradation in performance beyond an acceptable threshold level. In other words, rated capacity may refer to the maximum level of activity a server is designed to be subjected to without complete failure of the server, failure of a component of the server, and/or degradation in the performance of the server beyond a defined threshold. The rated capacity may be expressed, for example, in the same units as one or more of the metrics monitored by the e-commerce platform. For example, the rated capacity of a server may be indicated as a maximum number of requests received per second. In some embodiments, the rated capacity may refer to a maximum recommended capacity as stated by a provider or manufacturer of the server. In some embodiments, the rated capacity is a value that is calculated by the e-commerce platform based on characteristics of the server, such as, for example, the number of processors included in the server and the speed of those processors.

The data facility 400 may further store data for routing of requests in a routing object 406. In some embodiments, the routing object may include a plurality of mapping definitions in a lookup table. A mapping definition may be defined for specifying a mapping of a parameter of a request associated with an online store to a server identifier included in the routing object. For example, a lookup table may, in at least some embodiments, be used to map a domain name of a request to a server identifier. By way of example, a lookup table may include associations such as: requests to “johnsapparel.com” are routed to “serverA.com”; requests to “janesapparel.com” are routed to “serverB.com”; requests to “dicksapparel.com” are routed to “192.0.2.0”. In this example, if the store identifier is “johnsapparel.com”, a router may use the lookup table to map the request to the server identified as “serverA.com”. The router may use the server identifier to forward the request to the server corresponding to that server identifier.

Although many of the above examples refer to an “object” when discussing a data structure, it will be appreciated that this does not necessarily restrict the present application to implementation using object-oriented programming languages, and does not necessarily imply that the data structure is of a particular type or format. Data structures may have different names in different software paradigms. An object may be illustrated as a single unit for ease of illustration, but may include a plurality of objects.

Reference will now be made to FIG. 6 , which shows, in flowchart form, a simplified example method 600 for balancing online stores amongst servers. The method 600 may be implemented by a system suitably programmed to carry out the functions described. The system may be configured to receive or respond to communications from one or more user devices. A user device may be implemented by a customer device, such as the customer device 150 of FIG. 1 . The system may include the example e-commerce platform 100 of FIG. 1 or 300 of FIG. 3 ; however, an implementation in an e-commerce platform is only one example. At least some of the operations may also be implemented on any device or server, as a stand-alone component or service that is external to an e-commerce platform. In some embodiments, the operations may be provided as a cloud computing service, a software as a service (SaaS), and the like. Other possibilities exist. For example, more broadly, the various operations and techniques described herein may be employed in other application domains than e-commerce. In a particular example, the subject matter of the present application could be used to balance running instances of applications or other manners of computing services such as, for example, various cloud based services.

The method 600 may include, in operation 602, monitoring a level of customer activity corresponding to a first online store.

A level of customer activity may refer to an amount or quantity of customer activity over a defined period of time. The level of customer activity may be monitored continuously over successive windows of time. The length of each window may be, for example, a day, hour or minute. In some embodiments, the level of customer activity may be monitored over windows of time, such as, for example, short windows like 10 seconds, 30 seconds, or a minute, or, more broadly, some other temporal window.

Monitoring a level of customer activity may include monitoring usage of an online store by a customer device. Monitoring usage of an online store may include monitoring a level of interaction of a user of a customer device with a user interface corresponding to an online store. By way of example, monitoring the level of customer activity may include monitoring manual actions taken by customers. In some embodiments, a request may indicate a manual action taken by a user of a customer device. For example, the e-commerce platform may receive user input from the customer device, via an actionable user interface element, for proceeding to checkout or purchasing a particular product immediately. For example, the request may indicate a user selection of a “Proceed to Checkout” button or a “Buy Now” button on a webpage of an online store hosted on the e-commerce platform. By way of another example, the e-commerce platform may receive user input via an actionable user interface element provided as a link. The link may be in the form of an image that is selectable or clickable by a user. The image may be an advertisement or promotion of a product. The request may indicate the selection of the image by the user. The e-commerce platform may count the number of requests received that indicate the selection of a particular user interface element. The rate at which the requests are received may generally be referred to as a “click rate” for that user interface element.

Monitoring a level of customer activity may include monitoring a level of usage of a resource by an online store. For example, the router or server may receive, from a customer device, a request corresponding to an online store. The request may be associated with or indicate an action to be taken by the server. The action may include or involve accessing a resource. The e-commerce platform may monitor the number of times that a particular resource is accessed in response to receiving, from customer devices, requests corresponding to the online store. The number of times a particular resource is accessed in response to individual requests may vary from one request to another. Examples of resources that may be associated with the request include a checkout function, order function, payment function, shipping rate function, tax rate function, credit card validation function, address validation function, postal or zip code validation function, order form validation function, order tracking function, order return function, currency conversion function, new customer registration function, and a chat function connecting a user of the user device with a customer service representative of an online store.

Monitoring a level of customer activity may include monitoring a level of browsing activity. Monitoring a level of browsing activity may include monitoring a traffic load. The traffic load for a particular online store may include a count of total requests, being received by the router or servers, corresponding to an online store, a particular page of an store (e.g. a product page or product category page), or a subset of pages of an online store. The traffic load may also include a count of total page views corresponding to a graphical user interface of the online store. The traffic load may also include a frequency of requests associated with a product or product category associated with the scheduled modification.

The level of browsing activity may also include a count of unique visitors to the online store. The number of unique visitors may be determined by counting the number of uniquely identified clients that are generating requests or page views within a defined time period. A uniquely identified client may be an instance of a browser client or a client device. A client may be identified using a unique identifier stored in a browser cookie and/or the IP address of the client device.

The level of browsing activity may also include a count of currently active browsing sessions. A browsing session may refer to a series of requests received from a particular client. A browsing session may end or “time out” when the server receives no further requests from the particular client for a defined time period.

More granular levels of browsing activity may be monitored. For example, a level of browsing activity may include a level of referral, shopping cart, checkout, order and payment activity. Monitoring each of these levels of activity may include monitoring a corresponding traffic load.

By way of example, monitoring a level of referral activity may include monitoring the number of requests received from a particular referral source. The particular referral source may be identified using referrer information included in the request. The referrer information may be in the form of a web address of a previous webpage from which a link to a currently requested webpage was followed. For example, the referrer may be a web address of a webpage, such as a social media or webmail webpage, that provides a click-through link to the requested webpage.

By way of another example, monitoring a level of shopping cart activity may include monitoring the number of requests to add a particular product to an online shopping cart.

By way of another example, monitoring a level of checkout activity may include monitoring the number of requests received in association with a checkout function of an online store.

By way of another example, monitoring a level of order activity may include monitoring a traffic load or a sales metric. The traffic load may include the number of requests received corresponding to orders. The sales metric may include the total value of products ordered.

By way of another example, monitoring a level of payment activity may also include monitoring a traffic load or a sales metric. The traffic load may include a count of requests received corresponding to order payments. The sales metric may include the total value of payments made.

A metric may be expressed as a rate such as, for example, a number of requests or quantity of sales per unit of time.

The system may store the monitored level of customer activity as historical analytics data in the store object 402 of FIG. 4 .

Reference will now be made to FIGS. 5A-F, which show historical analytics data in graphic form. More particularly, the graphs show example results of monitoring the level of customer activity for example online stores. Each graph shows a sales metric, namely daily sales, for a respective example online store over a period of time.

In operation 604, the system may obtain a policy. The policy may be stored in memory that is accessible by the system. In some embodiments, the policy may be retrieved from the data facility 134. The policy may specify, for example, a threshold level of customer activity.

In operation 606, the system detects a flash sale associated with a first online store. The term “flash sale” may refer generally to a spike in order requests received by an online store. The flash sale may occur for a short period of time that may last a few seconds, minutes or hours.

The detection may be based on the observed level of customer activity and may involve a comparison with the threshold specified by the policy. For example, the observed level of customer activity may be compared with a threshold rate specified in the policy. If the comparison indicates that the observed level of customer activity exceeds the threshold, then a flash sale may be detected.

A level of customer activity may be a current, recent, or historical level of customer activity. A current level of customer activity may include the level of customer activity corresponding to a window of time that includes the current time. A recent level of customer activity may include the level of customer activity corresponding to one or more windows of time immediately preceding a current window of time. The historical level of customer activity for an online store may include levels of customer activity corresponding to historical windows of time, which may include windows of time prior to the current window of time.

An online store may have a “normal” level of customer activity. The normal level of customer activity may be determined based on historical levels of customer activity. In some cases, an average or median historical level of customer activity may be referred to as a normal level of customer activity.

The system may compare a current, recent or historical level of customer activity of the online store to a normal level of customer activity. Both the level of customer activity and the threshold may be specific to an online store, product, or type of product. For example, the level of customer activity may include a rate of order requests received corresponding to a particular product and the threshold may be a threshold rate specific to that particular product. As another example, the threshold ratio for an e-commerce platform or an online store may be 10:1. If the system determines that the current rate of order requests is at least ten times greater than a normal rate of order requests, then a flash sale may be detected.

Detecting a flash sale may also include determining whether the change in the level of customer activity, from a normal level of customer activity to the observed level of customer activity, is sudden. In other words, the detection may be based on determining that the rate of change in the level of customer activity exceeds a threshold rate of change. For example, the system may measure the time that has elapsed from when the normal level of customer activity was last observed to the current time. The elapsed time may be compared with a threshold time. If the elapsed time is less than the threshold time, then a flash sale event may be detected.

Reference is now made to FIGS. 5A-F, which show historical levels of customer activity for example online stores. In these examples, the daily sales for example online stores are shown over a period of time. In these examples, if a threshold ratio of 10:1 is used, various spikes in sales as shown in the graphs of FIGS. 5A-E may be detected as an anticipated flash sale event, whereas the sale metric shown in the graph of FIG. 5F has a current to normal ratio that peaks at about 4:1, which would not be considered sufficient to detect an anticipated flash sale event.

In operation 604, the server may identify an occurrence of a first event correlated to the flash sale associated with the first online store.

An event may be internal or external to the first online store.

An internal event may include a modification to an online store. The modification may be a scheduled event and may enable the first online store to receive orders for a product and may include one or more modification events. The modification may include a modification event for a product, an interface modification event, a data modification event, and/or a security modification event.

Examples of a modification event for a product include a product modification status event, a product modification listing event, a product modification inventory event, a product modification availability event, a product modification pricing event, and a product release event.

A product modification status event may include modifying the status indicator of a product. A status indicator may enable a merchant to control whether the corresponding product is available in the online store. The status may be “hidden”, “displayed”, “available soon” or “active”. The product status on newly created products may be set to “inactive” by default. A “hidden” status may indicate that the product is not displayed in a user interface, a “displayed” status may indicate that the product is displayed in a user interface, an “available soon” status may indicate that the product is displayed in a user interface but not available for purchase through the interface, and an “active” status may indicate that the product is displayed and available for purchase through a user interface.

A product modification inventory event may include a restocking event that occurs after an item is out-of-stock. In particular, the server may modify the inventory status for an item from being out-of-stock to being stocked. For example, an item that became out-of-stock during a flash sale may be scheduled to be restocked. In some embodiments, a product modification inventory event may include modifying an inventory level of a product by, for example, updating the inventory level from zero to a quantity greater than zero.

A product modification listing event may include modifying a product catalog to include a product. A product modification availability event may include modifying the status of a product from “inactive” to “active”. A product modification pricing event may include modifying the price of a product by discounting the sales price.

A product modification event may include a data modification event. For example, a database record storing the product status, inventory level and price may be updated as part of a product modification event.

A security modification event may include removing a form of security that prevents access to a particular webpage. For example, the event may include removing password protection or other form of authentication that is required for accessing a webpage associated with a product.

An interface modification event may include modifying an online store interface provided by the system. The interface may be updated to, for example, add to the interface a webpage corresponding to a newly released product, display a different price of a product, display an additional message, modify a currently displayed message, or add an image promoting a product or other graphical user interface element associated with a product. For example, a “Buy Now” or “Add to cart” button may be added to the interface. The event may also modify displayed text by, for example, amending displayed text that reads “Available soon” to “Now available”. The event may also configure a graphical user interface element to receive input. For example, the event may enable the selection of a “Buy Now” button that was previously displayed but was not configured for receiving input. The event may also change a color included in a graphical user interface element. For example, at least a portion of a “Buy Now” button may include a grey color that is changed to another color in order to indicate that the button is now selectable.

A product release event may involve configuring one or more modification events that enable the online store to receive orders for a product. For example, the product release event may include a product modification status event that modifies the status indicator from “hidden” or “available soon” to “active” and an interface modification event that adds to a user interface one or more graphical user interface elements associated with the product, such as an image of the product and/or an “Add to Cart” button that enables the receipt of user input indicating the selection of a product to add to a shopping cart. By way of another example, the product release event may include a security modification event that removes password protection from a webpage that includes one or more graphical user interface elements associated with the product.

The modification may be scheduled by a merchant via an administration console for the online store. In some embodiments, the merchant may upload a bulk file that includes scheduling information for one or more events. The scheduling information may include details identifying a modification event and a time for scheduling the modification. For example, the bulk file may include a list of new products to add to the store's catalog along with the details for scheduling the release of each product, such as a date on which the product is to be made available for purchase through the online store.

An external event may correspond to, for example, a disaster event and/or a social media event.

A disaster event may correspond to a natural disaster or a catastrophic emergency. A natural disaster event may include a sudden and terrible event brought about by nature, for example, a hurricane, tornado, flood or other severe weather event. A catastrophic emergency may include a pandemic, an armed conflict, or nuclear or chemical contamination. Other disaster events may include a state or provincial government evacuation order or a severe weather forecast. The severe weather forecast may include a weather classification, such as a category three hurricane.

A social media event may correspond to a post made on an online messaging service. The post may be a tweet created by an individual or organization associated with the first online store such as, for example, an influencer. The post may include the name of a product and may include a link to a webpage corresponding to a user interface associated with the first online store. The social media event may also include a recommendation or promotion of a particular product. By way of another example, a social media event may include an online challenge requiring participants of the challenge to obtain specific products offered for sale by the first online store. Such events may lead to viral activity on a social media platform that in turn leads to to a flash sale.

To identify an occurrence of a first event correlated to the flash sale associated with the first online store, the system may identify a product or product category associated with the flash sale.

The identification of an occurrence of the first event correlated to the flash sale may be based on a directory that maps events to products and/or product categories. The directory may indicate that a particular event may lead to a flash sale associated with a particular product or product category. In other words, the system may store a directory that maps events to products and/or product categories. The mapping can be used to identify a correlation between an event and a flash sale associated with a product or product category. For example, the directory may include a broad “emergency supplies” product category and narrower subcategories such “bottled water”, “batteries”, “flashlights” and “canned food”. These product categories and subcategories may be mapped to “hurricane” or “tornado” events. As another example, the product category “construction materials for sealing windows and doors” may be mapped to an “evacuation order” event. As yet another example, the product category “cosmetics” may be mapped to a “social media post by Celebrity X” event, where “Celebrity X” is the name of a celebrity.

In some embodiments, the mappings may be more granular. For example, products and product categories may be mapped to categories of hurricanes, since the products and product categories associated with flash sales may vary depending on the classification of the hurricane. A few products may map to a category two hurricane event and more products may map to a category three hurricane event.

Once the mapped events are determined, the system may identify an occurrence of the mapped event that took place prior to the flash sale and within a threshold length of time before flash sale was detected.

The system hosting the online store may identify the occurrence and timing of an internal event from internal data, application logs, server logs, host logs, scheduler events, scheduler information, and historical analytics data.

The system hosting the online store may identify the occurrence and timing of an external event from data obtained external to the system. Web scraping of external websites, such as government disaster agency websites, news websites, or social media websites may be performed to determine the occurrence of an external event.

The platform may also store rules for determining whether an event is correlated to a flash sale. For example, a rule may be that if a known social media influencer associated with the first online store posts a social media link corresponding to the first online store and a flash sale follows, that particular post may be identified as an occurrence of a first event correlated to the flash sale.

In operation 610, the server may identify a first geographic region. The geographic region may include a country, state/province, regional municipality, city and/or postal/zip code. The first geographic region may correspond to a region in which the flash sale is detected and/or a region in which the first event occurs or is identified in.

The geographical region may be associated with and serviced by the first online store and may correspond to one or more details of the first online store, the first event and/or the flash sale. The details may be used to identify the first geographic region.

For example, the identification may be based on an Internet country code top-level domain (ccTLD) of a domain name corresponding to the online store. Examples of ccTLDs include .cn, .de, .uk and .fr.

As another example, the identification may be based on a location of the merchant and/or a customer of the online store. The location may be a geographic address (e.g., street address) of the merchant and/or customers of the online store. The geographic address of the merchant may include contact information stored in a user account associated with the merchant and/or contact information of the online store that is provided to customer devices through a user interface. The geographic address of the customer may include a billing or shipping information included in an order requested received from a customer device.

The location of a customer may also be determined based on an IP address included in a request received by the system from a customer device. The IP address may be used to lookup a corresponding IP address location. The IP address location may include a geolocation, country, state/province, city and postal/zip code of a device. A geolocation may include a latitude, longitude and altitude. Reverse geocoding may be used to convert geographic coordinates to a geographic address.

The location of a customer may also be determined based on a geolocation included in a request received by the system from a customer device. In some implementations, the user device may include a satellite navigation device, sometimes referred to as a global positioning system (GPS) receiver, that is capable of receiving information from satellites and which may be used to determine a geolocation for that device.

The identification may also be based on an IP address of a server hosting the online store. The IP address may be used to lookup a corresponding geographic location.

The identification may also be based on language and currency details corresponding to the first online store, the first event and/or the flash sale. For example, the system may map a language or currency settings of the online store to a geographic region. By way of example, a lookup table may, in at least some instances, be used to map a language identifier or a currency identifier to one or more geographic region identifiers.

In operation 612, the server may identify a second online store associated with a second event corresponding to the first event. The second online store may be the same online store as the first online store or may be a different online store.

In some embodiments, the second event may correspond to the same type of event as the first event. For example, both events may correspond to earthquake events. The first event may correspond to a first earthquake and the second event may correspond to a subsequent earthquake months later, either in the same geographic region or a different one.

Identifying the second online store may include identifying a product or product category associated with the flash sale correlated to the occurrence of the first event. Once the product or product category is identified, the second online store may be identified based on that product or product category. The identification may include determining that the product or product category associated with the flash sale is associated with the second online store. For example, identification may include determining that the product or product category of the flash sale corresponds to a product or product category listed in a product catalog or displayed in a user interface associated with the second online store.

Identifying the second online store may include comparing a first profile associated with the first online store with a second profile associated with the second online store and determining, based on the comparison, that at the respective profiles match with at least a threshold level of confidence. A profile of a store may include a type or category of store, for example, a “disaster relief” category or a “hardware” category.

The identification of the second online store may be performed by searching through the product catalogs of the online stores hosted by the system in order to locate an online store including in its product catalog a product or product category matching the product or product category associated with the flash sale. The profiles of the hosted stores may also be searched for a profile that matches the first online store. Once a match is found, the associated online store may be identified as the second online store.

In some embodiments, the second event may include a prediction based on the first event. The prediction may correspond to an online store, referred to as the second online store, that is associated with the second geographic region that is different from the first geographic region associated with the first online store. In particular, the second event may include a predicted flash sale event. For example, the first event may include a social media event that correlates to a flash sale detected around 7 a.m. Central European Time (CET) in France. The second event may include a prediction that the first event is likely to lead to flash sale in the east coast of the United States of America at around 7 a.m. Eastern Standard Time (EST), when people there wake up and become informed of the social media event.

In operation 614, the server may identify a second geographic region that is different from the first geographic region. The second geographical region may be associated with and serviced by the second online store and may correspond to one or more details of the second online store and/or the second event. The details may be used to identify the second geographic region. Aspects of identifying the second geographic region in operation 614 may correspond to at least some aspect of identifying the first geographic region in operation 610.

In operation 616, the server may schedule moving the second online store from a first server to a second server. The move may be scheduled to occur at a time that is earlier than a time at which the second event is expected to occur or actually occurs.

The move may be scheduled to correspond with time zones of the first and second geographic regions. The first and second geographic regions may be mapped to different respective time zones. By way of example, a lookup table may, in at least some instances, be used to map the first geographic region to a first time zone and the second geographic region to a different time zone. The timing of the move may be based on the first time zone corresponding to the first geographical region and the second time zone corresponding to the second geographical region.

The system may track a first local time of the first geographic region at which the flash sale in operation 606 was detected and schedule the move to occur at a second local time of the second geographic region that is earlier than the first local time. For example, if the first geographic region is France and the first local time is 7 a.m. CET, and the second geographic region is the east coast of the United States the second local time may be before 7 a.m. EST. In other words, the time difference between the first and second time zones may be determined and the move may be scheduled to occur before the length of the time difference after the time of the detected flash sale lapses. Put another way, if the time difference is six hours, the move may be scheduled to occur at a time that is no more than six hours after the time at which the flash sale commences. In this way, the move may be scheduled to occur before a flash sale is predicted to occur.

In operation 618, the server may, responsive to identifying the second store associated with the second event corresponding to the first event, move the second store from a first server to a second server.

The second online store may be one particular online store of a plurality of online stores. Each online store may be served from one of a plurality of servers. Each of the plurality of servers may host more than one online store.

The first and second servers may have the same rated capacity or may have different capacity ratings. A rated capacity of the first server may be less than or greater than a rated capacity of the second server. In other words, the first server may include increased or decreased computing resources than the second server. Computing resources may include, for example, a quantity or speed of processors or memory. Put another way, the first server may be configured for servicing a predetermined load higher than a predetermined load of the second server, or vice versa.

The move may be performed in order to increase the resources available to the second online store. In some embodiments, an unused capacity of the first server immediately prior to the move may be less than an unused capacity of the second server immediately after the move. In this way, the server resources and unused capacity available to the second online store may be increased.

Moving an online store from a first server to a second server may refer generally to moving the servicing of requests from the first server to the second server. In other words, it may refer to modifying the system to use the second server to service customer requests corresponding to the online store instead of the first server. The first server ceases to service requests associated with the online store and the second server commences servicing requests associated with the online store.

The system may configure a router to cease directing requests corresponding to the online store to the first server and queue any new requests until the second server is ready to begin servicing requests associated with the online store. The first server may finish servicing any requests that it has already received in association with the online store and provide responses to customer devices. The second server may be configured to service requests corresponding to the online store. This operation may depend on how the online store is deployed.

In some embodiments, the online store is a store hosted on an e-commerce platform that is provided as SaaS. In other words, the online store may be deployed to a running instance of an e-commerce software application that provides online store hosting as a service. Moving the online store may include transferring assets (e.g. configuration files, database shards, database records, image files) associated with the online store either from the first server and/or a different server and configuring an e-commerce software application hosted on the second server with those assets in order to serve the online store from the second server. In some embodiments, an asset may include the store object 402 of FIG. 4 or a portion thereof.

In some embodiments, not all assets corresponding to an online store are stored on the first or second servers or accessible by these servers. For example, static store assets such as image files may be served from a separate file server. Moving an online store from a first server to a second server may include copying only assets that are stored on the first server to the second server. Similarly, not all requests corresponding to an online store that are received by the system may be serviced by the first or second servers. Other requests, such as order requests or payment requests, may be serviced by the first or second servers. Moving an online store from a first server to a second server may include directing to the second server and servicing by the second server any requests associated with the online store that would otherwise have been directed to and serviced by the first server.

In some embodiments, the online store may be a web application that is deployed to a first installation instance of a web server application hosted on the first server. Moving such an online store may include deploying the web application to a second installation instance of the web server application hosted on the second server.

In some embodiments, the online store may be hosted on a virtual server that is distinct and separate from virtual servers hosting other online stores. Moving such an online store may involve stopping a virtual server that hosts the online store on a first physical server, copying a virtual server file corresponding to the virtual server from the first physical server to a second physical server, and restarting the virtual server on the second physical server.

Once the second server has been configured to service requests corresponding to the online store, the router may be configured to direct requests corresponding to the online store to the second server instead of the first server by updating routing data. The second server may then receive and service requests associated with the online store.

Implementations

The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor. The processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like. The processor may be or include a signal processor, digital processor, embedded processor, microprocessor or any variant such as a co-processor (math co-processor, graphic co-processor, communication co-processor and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, program codes, program instructions and the like described herein may be implemented in one or more threads. The thread may spawn other threads that may have assigned priorities associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor may include memory that stores methods, codes, instructions and programs as described herein and elsewhere. The processor may access a storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.

A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In some embodiments, the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (called a die).

The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software on a server, cloud server, client, firewall, gateway, hub, router, or other such computer and/or networking hardware. The software program may be associated with a server that may include a file server, print server, domain server, internet server, intranet server and other variants such as secondary server, host server, distributed server and the like. The server may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.

The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers and the like. Additionally, this coupling and/or connection may facilitate remote execution of programs across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without deviating from the scope of the disclosure. In addition, any of the devices attached to the server through an interface may include at least one storage medium capable of storing methods, programs, code and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

The software program may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client, distributed client and the like. The client may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like. The methods, programs or codes as described herein and elsewhere may be executed by the client. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers and the like. Additionally, this coupling and/or connection may facilitate remote execution of programs across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without deviating from the scope of the disclosure. In addition, any of the devices attached to the client through an interface may include at least one storage medium capable of storing methods, programs, applications, code and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices and other active and passive devices, modules and/or components as known in the art. The computing and/or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM and the like. The processes, methods, program codes, instructions described herein and elsewhere may be executed by one or more of the network infrastructural elements.

The methods, program codes, and instructions described herein and elsewhere may be implemented in different devices which may operate in wired or wireless networks. Examples of wireless networks include 4th Generation (4G) networks (e.g. Long Term Evolution (LTE)) or 5th Generation (5G) networks, as well as non-cellular networks such as Wireless Local Area Networks (WLANs). However, the principles described therein may equally apply to other types of networks.

The operations, methods, programs codes, and instructions described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players and the like. These devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute program codes, methods, and instructions stored thereon. Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices. The mobile devices may communicate with base stations interfaced with servers and configured to execute program codes. The mobile devices may communicate on a peer to peer network, mesh network, or other communications network. The program code may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store program codes and instructions executed by the computing devices associated with the base station.

The computer software, program codes, and/or instructions may be stored and/or accessed on machine readable media that may include: computer components, devices, and recording media that retain digital data used for computing for some interval of time; semiconductor storage known as random access memory (RAM); mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory; optical storage such as CD, DVD; removable media such as flash memory (e.g. USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, off-line, and the like; other computer memory such as dynamic memory, static memory, read/write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.

The methods and systems described herein may transform physical and/or or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and/or intangible items from one state to another, such as from usage data to a normalized usage dataset.

The elements described and depicted herein, including in flow charts and block diagrams throughout the figures, imply logical boundaries between the elements. However, according to software or hardware engineering practices, the depicted elements and the functions thereof may be implemented on machines through computer executable media having a processor capable of executing program instructions stored thereon as a monolithic software structure, as standalone software modules, or as modules that employ external routines, code, services, and so forth, or any combination of these, and all such implementations may be within the scope of the present disclosure. Examples of such machines may include, but may not be limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipment, servers, routers and the like. Furthermore, the elements depicted in the flow chart and block diagrams or any other logical component may be implemented on a machine capable of executing program instructions. Thus, while the foregoing drawings and descriptions set forth functional aspects of the disclosed systems, no particular arrangement of software for implementing these functional aspects should be inferred from these descriptions unless explicitly stated or otherwise clear from the context. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein. All such variations and modifications are intended to fall within the scope of this disclosure. As such, the depiction and/or description of an order for various steps should not be understood to require a particular order of execution for those steps, unless required by a particular application, or explicitly stated or otherwise clear from the context.

The methods and/or processes described above, and steps thereof, may be realized in hardware, software or any combination of hardware and software suitable for a particular application. The hardware may include a general-purpose computer and/or dedicated computing device or specific computing device or particular aspect or component of a specific computing device. The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices, along with internal and/or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine readable medium.

The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software, or any other machine capable of executing program instructions.

Thus, in one aspect, each method described above, and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. All such permutations and combinations are intended to fall within the scope of the present disclosure. 

1. A computer-implemented method of balancing online stores amongst servers, the method comprising: detecting a flash sale associated with a first online store; identifying an occurrence of a first event correlated to the flash sale associated with the first online store; identifying a second online store associated with a second event corresponding to the first event; and responsive to identifying the second online store associated with the second event corresponding to the first event, moving the second online store from a first server to a second server.
 2. The method of claim 1, wherein identifying the second online store associated with the second event corresponding to the first event includes: identifying a product or product category associated with the flash sale correlated to the occurrence of the first event; and identifying the second online store based on the product or product category.
 3. The method of claim 1, wherein identifying the second online store associated with the second event corresponding to the first event includes: comparing a first profile associated with the first online store with a second profile associated with the second online store; and determining, based on the comparison, that at the respective profiles match with at least a threshold level of confidence.
 4. The method of claim 1, wherein the first event includes a modification to the first online store.
 5. The method of claim 1, wherein the first event is external to the first online store.
 6. The method of claim 1, wherein the first event corresponds to a natural disaster.
 7. The method of claim 1, wherein the first event corresponds to a recommendation of a product.
 8. The method of claim 1, wherein identifying the occurrence of the first event correlated to the flash sale includes obtaining data from a webpage or website external to the first online store.
 9. The method of claim 1, wherein the first and second online stores are different online stores.
 10. The method of claim 1, wherein the first and second online stores are the same online store.
 11. The method of claim 1, wherein the second event commences after the first event.
 12. The method of claim 1, wherein the second event includes a prediction based on the first event.
 13. The method of claim 12, wherein the prediction corresponds to the second online store associated with a second geographical region different from a first geographic region associated with the first online store.
 14. The method of claim 1, wherein detecting the flash sale includes detecting the flash sale in a first geographical region.
 15. The method of claim 1, wherein identifying an occurrence of a first event includes identifying an occurrence of a first event in a first geographic region.
 16. The method of claim 15, wherein identifying an occurrence of a first event correlated to the flash sale associated with the first online store includes mapping a product or product category associated with the flash sale to the first online store.
 17. A system, the system comprising: a processor; and a memory storing computer-executable instructions that, when executed by the processor, are to cause the processor to: detect a flash sale associated with a first online store; identify an occurrence of a first event correlated to the flash sale associated with the first online store; identify a second online store associated with a second event corresponding to the first event; and responsive to identifying the second online store associated with the second event corresponding to the first event, move the second online store from a first server to a second server.
 18. The system of claim 17, wherein the instructions, when executed by the processor, are to cause the processor to: identify a product or product category associated with the flash sale correlated to the occurrence of the first event; and identify the second online store based on the product or product category.
 19. The system of claim 17, wherein the instructions, when executed by the processor, are to cause the processor to: compare a first profile associated with the first online store with a second profile associated with the second online store; and determine, based on the comparison, that at the respective profiles match with at least a threshold level of confidence.
 20. A non-transitory computer-readable medium storing processor-executable instructions that, when executed by one or more processors, are to cause the one or more processors to: detect a flash sale associated with a first online store; identify an occurrence of a first event correlated to the flash sale associated with the first online store; identify a second online store associated with a second event corresponding to the first event; and responsive to identifying the second online store associated with the second event corresponding to the first event, move the second online store from a first server to a second server. 